# US 2022/0044179 A1 · Optimized estimates for support characteristics for operational systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044179 A1 - Document ID: US20220044179A1 - Status: Published application - Kind: A1 - Application number: 16/984,581 - Filed: 2020-08-04 - Published: 2022-02-10 - Earliest claimed: 2020-08-04 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Pranshu Tiwari; HARISH BHARTI; PRADEEP DASH; SANTOSH PATIL; Swarnalata Patel; Saurabh Trehan - Web page: https://patentgrep.com/patent/US20220044179A1 ## CPC - G06Q 10/06375: {Prediction of business process outcome or impact based on a proposed change} - G06F 16/288: {Entity relationship models} - G06N 5/02: Knowledge representation; Symbolic representation - G06N 20/00: Machine learning - G06Q 10/063112: {Skill-based matching of a person or a group to a task} - G06Q 10/105: Human resources - G06Q 30/016: After-sales - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method, system, and computer program product for predicting optimal service characteristics to execute predicted IT tickets. The method may include identifying deployment characteristics for an operational system based on an architecture of the operational system. The method may also include receiving implementation data in response to past incident tickets based on the deployment characteristics. The method may also include identifying relationships within the implementation data. The method may also include predicting a volume of new tickets based on the implementation data and the relationships between the implementation data. The method may also include predicting a resolution time for high severity tickets in the volume of new tickets. The method may also include determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets. The method may also include transmitting a recommendation comprising the preferred service characteristics. ## Claims ### Claim 1 A computer-implemented method comprising: identifying deployment characteristics for an operational system based on an architecture of the operational system; receiving implementation data regarding past implementations in response to past incident tickets based on the deployment characteristics; identifying relationships within the implementation data; predicting a volume of new tickets based on the implementation data and the relationships between the implementation data; predicting a resolution time for high severity tickets in the volume of new tickets; determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets, wherein the preferred service characteristics decrease the resolution time; and transmitting a recommendation comprising the preferred service characteristics. ### Claim 2 (depends on 1) The method of claim 1, wherein identifying the deployment characteristics for the operational system comprises: identifying an architecture for an operational system; and determining the deployment characteristics specific to the architecture for the operational system. ### Claim 3 (depends on 2) The method of claim 2, wherein: the architecture for the operational system is an on-premises architecture; and the determined deployment characteristics comprise at least one of a number of business users, a cyclomatic complexity, an elapsed performance time, and a number of integrations. ### Claim 4 (depends on 2) The method of claim 2, wherein: the architecture for the operational system is a cloud-based architecture; and the determined deployment characteristics comprise at least one of a number of business users, number of services, number of containers, code base, number of microservice templates, heterogeneous protocols, number of HTTP operations, deployment operations, static or dynamic forms, number of technical components, message architecture, cloud models, number of integration systems, and number of translations. ### Claim 5 (depends on 1) The method of claim 1, wherein predicting the volume of new tickets comprises: executing a plurality of regression models to predict the volume of new tickets; determining a best-fit model from the plurality of regression models; performing cross-validation on the best-fit model; and determining whether the best-fit model is over-fit based on the cross-validation. ### Claim 6 (depends on 5) The method of claim 5, further comprising: calculating a variance inflation factor for each regression model from the plurality of regression models; calculating an error rate for each regression model; and comparing the variance inflation factor and the error rate for each regression model. ### Claim 7 (depends on 5) The method of claim 5, further comprising: in response to determining that the best-fit model is over-fit, determining a second best-fit model from the plurality of regression models. ### Claim 8 (depends on 5) The method of claim 5, further comprising: in response to determining that the best-fit model is not over-fit, executing the best-fit model as a prediction model. ### Claim 9 (depends on 1) The method of claim 1, wherein the preferred service characteristics comprises at least time, experience, skillset, qualification, and cost of a candidate support worker. ### Claim 10 (depends on 1) The method of claim 1, further comprising: determining a probability of the high severity tickets from the predicted volume of new tickets. ### Claim 11 (depends on 10) The method of claim 10, wherein determining the preferred service characteristics is further based on the probability of the high severity tickets. ### Claim 12 A system having one or more computer processors, the system configured to: identify deployment characteristics for an operational system based on an architecture of the operational system; receive implementation data regarding past implementations in response to past incident tickets based on the deployment characteristics; identify relationships within the implementation data; predict a volume of new tickets based on the implementation data and the relationships between the implementation data; predict a resolution time for high severity tickets in the volume of new tickets; determine preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets, wherein the preferred service characteristics decrease the resolution time; and transmit a recommendation comprising the preferred service characteristics. ### Claim 13 (depends on 12) The system of claim 12, wherein identifying the deployment characteristics for the operational system comprises: identifying an architecture for an operational system; and determining the deployment characteristics specific to the architecture for the operational system. ### Claim 14 (depends on 13) The system of claim 13, wherein: the architecture for the operational system is an on-premises architecture; and the determined deployment characteristics comprise at least one of a number of business users, a cyclomatic complexity, an elapsed performance time, and a number of integrations. ### Claim 15 (depends on 13) The system of claim 13, wherein: the architecture for the operational system is a cloud-based architecture; and the determined deployment characteristics comprise at least one of a number of business users, number of services, number of containers, code base, number of microservice templates, heterogeneous protocols, number of HTTP operations, deployment operations, static or dynamic forms, number of technical components, message architecture, cloud models, number of integration systems, and number of translations. ### Claim 16 (depends on 12) The system of claim 12, wherein predicting the volume of new tickets comprises: executing a plurality of regression models to predict the volume of new tickets; determining a best-fit model from the plurality of regression models; performing cross-validation on the best-fit model; and determining whether the best-fit model is over-fit based on the cross-validation. ### Claim 17 A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a server to cause the server to perform a method, the method comprising: identifying deployment characteristics for an operational system based on an architecture of the operational system; receiving implementation data regarding past implementations in response to past incident tickets based on the deployment characteristics; identifying relationships within the implementation data; predicting a volume of new tickets based on the implementation data and the relationships between the implementation data; predicting a resolution time for high severity tickets in the volume of new tickets; determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets, wherein the preferred service characteristics decrease the resolution time; and transmitting a recommendation comprising the preferred service characteristics. ### Claim 18 (depends on 17) The computer program product of claim 17, wherein identifying the deployment characteristics for the operational system comprises: identifying an architecture for an operational system; and determining the deployment characteristics specific to the architecture for the operational system. ### Claim 19 (depends on 17) The computer program product of claim 17, wherein predicting the volume of new tickets comprises: executing a plurality of regression models to predict the volume of new tickets; determining a best-fit model from the plurality of regression models; performing cross-validation on the best-fit model; and determining whether the best-fit model is over-fit based on the cross-validation. ### Claim 20 (depends on 19) The computer program product of claim 19, further comprising: calculating a variance inflation factor for each regression model from the plurality of regression models; calculating an error rate for each regression model; and comparing the variance inflation factor and the error rate for each regression model.